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7. Discussion Trees on Social Media: A New Approach to Detecting Antisemitism Online

Chapter of: Antisemitism in Online Communication: Transdisciplinary Approaches to Hate Speech in the Twenty-First Century(pp. 185–204)

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Metadata
Title7. Discussion Trees on Social Media
SubtitleA New Approach to Detecting Antisemitism Online
ContributorChloé Vincent(author)
DOIhttps://doi.org/10.11647/obp.0406.07
Landing pagehttps://www.openbookpublishers.com/books/10.11647/obp.0406/chapters/10.11647/obp.0406.07
Licensehttps://creativecommons.org/licenses/by/4.0/
CopyrightChloé Vincent
PublisherOpen Book Publishers
Published on2024-06-21
Long abstractAntisemitism often takes implicit forms on social media, therefore making it difficult to detect. In many cases, context is essential to recognise and understand the antisemitic meaning of an utterance (Becker et al. 2021, Becker and Troschke 2023, Jikeli et al. 2022a). Previous quantitative work on antisemitism online has focused on independent comments obtained through keyword search (e.g. Jikeli et al. 2019, Jikeli et al. 2022b), ignoring the discussions in which they occurred. Moreover, on social media, discussions are rarely linear. Web users have the possibility to comment on the original post and start a conversation or to reply to earlier web user comments. This chapter proposes to consider the structure of the comment trees constructed in the online discussion, instead of single comments individually, in an attempt to include context in the study of antisemitism online. This analysis is based on a corpus of 25,412 trees, consisting of 76,075 Facebook comments. The corpus is built from web comments reacting to posts published by mainstream news outlets in three countries: France, Germany, and the UK. The posts are organised into 16 discourse events, which have a high potential for triggering antisemitic comments. The analysis of the data help verify whether (1) antisemitic comments come together (are grouped under the same trees), (2) the structure of trees (lengths, number of branches) is significant in the emergence of antisemitism, (3) variations can be found as a function of the countries and the discourse events. This study presents an original way to look at social media data, which has potential for helping identify and moderate antisemitism online. It specifically can advance research in machine learning by allowing to look at larger segments of text, which is essential for reliable results in artificial intelligence methodology. Finally, it enriches our understanding of social interactions online in general, and hate speech online in particular.
Page rangepp. 185–204
Print length204 pages
LanguageEnglish (Original)
Contributors

Chloé Vincent

(author)
Expert in quantitative analysis, French team of Decoding Antisemitism, Researcher at ZfA at Technische Universität Berlin
PhD Candidate at Ghent University
https://orcid.org/0009-0004-6622-4303

Chloé Vincent is currently preparing a PhD at Ghent University on how gender neutral pronouns in French affect text quality and mental gender representations. She previously worked on the French team of the Decoding Antisemitism project as a researcher and expert in quantitative analysis. She completed her MA in Linguistics at Queen Mary University of London in September 2020 where she specialised in sociolinguistics, learning both quantitative and qualitative methods. Her master’s thesis consisted in analysing the attitudes of native French speakers towards French regional accents. In the previous years, she had completed her undergraduate degree in anthropology at Lumière University Lyon 2, and a degree in French language teaching at Grenoble Alpes University, while working as a software developer. She also holds a Master of Engineering degree from Grenoble INP Graduate School of Engineering.

References
  1. Becker, Matthias J., Daniel Allington, Laura Ascone, Matthew Bolton, Alexis Chapelan, Jan Krasni, Karolina Placzynta, Marcus Scheiber, Hagen Troschke and Chloé Vincent, 2021. Decoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online. Discourse Report 2. Technische Universität Berlin. Centre for Research on Antisemitism, https://doi.org/10.14279/depositonce-15310
  2. Becker, Matthias J., and Hagen Troschke, 2023. “Decoding implicit hate speech: The example of antisemitism”. In: Christian Strippel, Sünje Paasch-Colberg, Martin Emmer and Joachim Trebbe (eds). Challenges and Perspectives of Hate Speech Research. Digital Communication Research, 335-352, https://doi.org/10.48541/dcr.v12.0
  3. Jikeli, Günther, Damir Cavar and Daniel Miehling, 2019. Annotating Antisemitic Online Content. Towards an applicable definition of antisemitism, https://arxiv.org/pdf/1910.01214, https://doi.org/10.5967/3r3m-na89
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  5. Jikeli, Günther, David Axelrod, Rhonda K. Fischer, Elham Forouzesh, Weejeong Jeong, Daniel Miehling and Katharina Soemer, 2022b. “Differences between antisemitic and non-antisemitic English language tweets”. Computational and Mathematical Organization Theory, 1-35, https://doi.org/10.1007/s10588-022-09363-2
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